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Estimate GPU needs, training costs, inference spend, and AI infrastructure TCO.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "infra-advisor-mcp" yet — see the docs or source repo.
Estimate the required GPU count, training duration, and cost based on the following: 7B-parameter model, 500B total training tokens, target cloud provider AWS, using A100 80GB instances. Include key assumptions.
Provides GPU requirements, estimated training timeline, cloud training cost, and key calculation assumptions.
Estimate resources and costs for an online inference service: 13B-parameter model, 2 million requests per day, average 800 input tokens and 200 output tokens per request, with p95 latency under 2 seconds. Compare cloud and on-prem deployment costs.
Provides inferred GPU sizing, throughput assumptions, and a cost comparison between cloud and on-prem deployment.
Create a 3-year TCO analysis for AI infrastructure planning: the team will continuously train and serve multiple mid-sized models, and is comparing a public cloud GPU cluster versus an on-prem data center buildout. Include hardware, operations, power, depreciation, and related cost items, then provide a recommendation.
Provides a TCO breakdown framework, key cost drivers, and a conclusion comparing cloud and on-prem options.
Parse multi-cloud IaC and generate real-time cost estimates and comparisons.
Analyze Azure Data Factory costs, detect waste, and recommend optimizations.
Query normalized usage, cost, and dashboard data across OpenAI and Anthropic.
Measure AI energy, water, CO₂ impact, and prompt efficiency from token usage.
View tenant-scoped AI credit usage data for admin users.
Build, debug, and manage software tasks with natural language across LLMs.